ARTIFICIAL INTELLIGENCE

8 min readSep 16, 2023

ARTIFICIAL INTELLIGENCE

Introduction

What is artificial intelligence?

Artificial Intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. AI is accomplished by studying the patterns of the human brain and by analyzing the cognitive process. Specific applications of AI include expert systems, a computer system emulating the decision-making ability of a human expert; natural language processing, which is concerned with giving computers the ability to understand text and spoken words in the same way as a human being; speech recognition; this is an application in AI that converts spoken language into text, and machine vision; gives the AI the ability to analyse tasks in intelligent manufacturing, quality control, and worker safety.

There are four types of AI. They are reactive machines; they have no memory and are task-specific. Limited memory, they have memory thus, they can use past experiences to inform their future predictions — for example, self-driving cars. Theory of mind: This AI can use social intelligence to understand human emotions. It will be able to predict behavior and human intentions. Self-awareness: This type of AI has a sense of self that gives them consciousness, which enables them to understand their current state. This type of AI does not exist yet.

How does it work?

Artificial Intelligence emphasizes three cognitive skills: learning, reasoning, and self-correction, skills the human brain possesses to one degree or another. In the context of AI, learning is the acquisition of information and the rules needed to use that information, reasoning is using the information rules to reach definite or approximate conclusions, and self-correction is the process of continually fine-tuning AI algorithms to ensure that they offer the most accurate results they can.

Implementing AI works hand in hand with machine learning and deep learning. It is machine learning that gives AI the ability to learn. This is done by using algorithms to discover patterns and generate insights from the data they are exposed to. Deep learning, a subcategory of machine learning, allows AI to mimic a human brain’s neural network. It can make sense of patterns, noise, and sources of confusion in the data.

INTERESTING FACT!

The concept of inanimate objects endowed with intelligence has existed since ancient times. The Greek god Hephaestus was depicted in myths as forging robot-like servants out of gold.

History of AI

In the 1940s and 50s, a handful of scientists from various fields discussed the possibility of creating an artificial brain. Artificial intelligence research was founded as an academic discipline in 1956. The time between when the phrase “artificial intelligence” was created and the 1980s was a period of rapid growth and struggle for AI research.

Notable dates include:

1958: John McCarthy created the first programming language for AI research, which is still widely used today.

1959: Arthur Samuel created the term ‘machine learning’ when doing a speech about teaching machines to play chess better than humans who programmed them.

1965: Edward Feigenbaum and Joshua Lederberg created the first expert system, a form of AI programmed to replicate human experts’ thinking and decision-making abilities.

1979: The American Association of Artificial Intelligence, now known as the Association for the Advancement of Artificial Intelligence (AAAI), was founded.

AI BOOM: 1980–1987

This came from both breakthroughs in research and additional government funding to support the researchers. Deep learning techniques and the use of expert systems became more popular, both of which allowed computers to learn from their mistakes and make independent decisions.

Notable dates in this period include:

1980: The first conference of the AAAI was held at Stanford.

1980: The first expert system came into the commercial market, known as XCON.

1984: The AAAI warns of an incoming “AI Winter.”

AI WINTER: 1987–1993

The term describes a period of low consumer interest in AI, leading to decreased research funding and few breakthroughs. Both private investors and the government lost interest in AI and halted funding due to high costs versus seemingly low returns. This came about because of some setbacks in the machine market and expert systems, cutbacks in strategic computing initiatives, and a slowdown in the deployment of expert systems.

Trends In AI

Natural Language Processing

NLP is experiencing continuous growth due to the need for computers to understand human languages better. Startups offer NLP-powered solutions for recognizing words, sentences, and parts of speech. For example, HR uses NLP-based intelligent assistants to improve response times and provide product-specific information. Additionally, NLP enables automated communication with people in their native language. This, in turn, scales other language-related tasks like email filters, text prediction, digital phone calls, and text analytics into multiple languages.

Robotic Process Automation

RPA robots perform repetitive, rules-based, predictable, and time-critical tasks, allowing businesses to improve operational efficiency and effectiveness. RPA also brings various benefits, such as financial benefits owing to low-cost robot licenses, improved accuracy, timeliness, and operational flexibility.

Computer Vision

Its algorithms understand pictures and videos, making automotive manufacturing their primary customer in autonomous vehicles. The application of this technology is also seen in medicine, precision agriculture, security, robotics, and more. More specific computer vision-based solutions include facial, emotion, gesture recognition, motion detection, and eye and object tracking. Further, this AI technology detects and tracks unsafe employee behavior and enables many applications, from cashier-less stores to virtual try-on experiences.

Dangers of AI

Due to the automation of various tasks, people face some vulnerability in the employment sector. AI-driven automation could have a significant economic impact in sectors like transport factories and customer service. Moreover, factories are shifting as labor costs are lower than employing human workers. This may boost productivity but at the cost of job loss. Some jobs are more vulnerable, specifically those that involve simple tasks, since they can be automated. Examples are the customer service roles being filled by AI chatbots, cashiers, and bank tellers being replaced by self-checkout machines and automated banking services. Looking ahead, there is a risk of generative AI specializing in critical thinking to make more complex job categories obsolete.

It is a common misconception that AI will primarily threaten blue-collar occupations. This impression is false. People working in offices should be concerned about their careers because AI is already capable of data analysis, strategic planning, and report creation. AI is influencing both administrative rules and manual tasks like lifting boxes. Additionally, automation brought on by AI might exacerbate pay disparity. People who struggle to make ends meet will lose their jobs as AI primarily replaces low-paying jobs. However, the wages for high-paying positions needing specialized skills can go up. The logic is that as AI takes over more tasks, jobs that still require human intervention will often demand specialized skills, which fewer people will have driven up their salaries because they will be in high demand. Due to this scenario, the already concerning wealth gap will increase.

Problems AI Has Helped to Solve

Although artificial intelligence is a relatively new technological development, it has made an instant impact in various sectors and aided in solving many problems in the modern world.

Healthcare

AI has significantly impacted the medical sector, especially in surgical operations. Man is prone to error during surgeries, leading to failed surgeries costing people their lives. Artificial intelligence has been implemented in these surgical operations and can perform these surgeries with or without man’s guidance and with minimal errors.

In addition, some surgeries can last up to 24 hours; regular human doctors require rest and cannot perform surgeries for prolonged times. This is where artificial intelligence comes in. These robots can perform the surgeries when doctors are resting without getting tired.

Security

AI is now widespread in the military. It can be implemented in many ways, such as detecting enemy missiles and intercepting them, gathering intelligence on enemies using unmanned aerial vehicles which do not require human control to use, hence reducing the risk of enemies spotting them as compared to before when spy planes had to be used by military pilots, and this would lead to their planes being shot down. AI is also used in cyber security. It can detect and intercept malicious hacks from unethical hackers compared to the pre-AI time when people were more prone to hackers. AI is also used in looking for criminals through facial scanning in public areas as compared to before when security personnel had to rely on witnesses who would sometimes not provide a proper description.

Agriculture & Natural Disaster Prediction

AI applications in agriculture include crop monitoring, yield prediction, and precision farming which help improve crop yields and optimise resource usage. AI models analyse various data sources, such as weather patterns, seismic data, and satellite imagery, to predict natural disasters like hurricanes, earthquakes, and floods. This helps in early warning and preparedness, especially for farmers who use the information to prepare their land.

References

Members involved in coming with this write up:

Mathenge, Alvin Kariuki

Saisi, Dylan Musalia

Otieno, Bill Ritchie

Wanyoike, Samuel Macharia

Krishna, Mahendra Madhaparia

Kemoi, Kristina Chebet

Munene, Gloria Kendi

STUDENTS AT STRATHMORE UNIVERSITY

Written by Alvin Mathenge